R Notebook

library(tidyverse)

Registered S3 methods overwritten by 'dbplyr':
  method         from
  print.tbl_lazy     
  print.tbl_sql      
── Attaching packages ──────────────────────────────────────────────────────────────────── tidyverse 1.3.0 ──
✓ ggplot2 3.3.3     ✓ purrr   0.3.4
✓ tibble  3.0.6     ✓ dplyr   1.0.5
✓ tidyr   1.1.3     ✓ stringr 1.4.0
✓ readr   1.4.0     ✓ forcats 0.5.1
── Conflicts ─────────────────────────────────────────────────────────────────────── tidyverse_conflicts() ──
x dplyr::filter() masks stats::filter()
x dplyr::lag()    masks stats::lag()

library(sf)

Linking to GEOS 3.8.1, GDAL 3.1.4, PROJ 6.3.1

library(rmapshaper)

Registered S3 method overwritten by 'geojsonlint':
  method         from 
  print.location dplyr

library(here)

here() starts at /Users/emilyjaco/codeclan_health_dashboard

library(leaflet)

Registered S3 method overwritten by 'htmlwidgets':
  method           from         
  print.htmlwidget tools:rstudio

library(htmltools)

scotland <- read_sf(here("data_raw/SG_NHS_HealthBoards_2019/SG_NHS_HealthBoards_2019.shp"))

class(scotland)

[1] "sf"         "tbl_df"     "tbl"        "data.frame"

head(scotland, 14)

Simple feature collection with 14 features and 4 fields
Geometry type: MULTIPOLYGON
Dimension:     XY
Bounding box:  xmin: 5512.998 ymin: 530250.8 xmax: 470332 ymax: 1220302
Projected CRS: OSGB 1936 / British National Grid

scotland_geometry <- st_geometry(scotland)

scotland_geometry

Geometry set for 14 features 
Geometry type: MULTIPOLYGON
Dimension:     XY
Bounding box:  xmin: 5512.998 ymin: 530250.8 xmax: 470332 ymax: 1220302
Projected CRS: OSGB 1936 / British National Grid
First 5 geometries:

MULTIPOLYGON (((201916.2 600444.4, 201918.4 600...
MULTIPOLYGON (((345325.9 579663.3, 345325.1 579...
MULTIPOLYGON (((266004.4 543544.6, 266010.9 543...
MULTIPOLYGON (((256533 740502, 256501.5 740379,...
MULTIPOLYGON (((383521.2 771234.7, 383524.3 771...

scotland_simple <- ms_simplify(scotland)

plot(scotland_simple)

alcohol <- read_csv("../data_clean/alcohol.csv") %>% 
  filter(sex == "All") %>% 
  filter(date_code == "2016-2019") %>% 
  filter(alcohol_consumption == "Hazardous/Harmful drinker")

── Column specification ─────────────────────────────────────────────────────────────────────────────────────
cols(
  feature_code = col_character(),
  date_code = col_character(),
  measurement = col_character(),
  units = col_character(),
  value = col_double(),
  alcohol_consumption = col_character(),
  sex = col_character(),
  hb_name = col_character(),
  longitude = col_double(),
  latitude = col_double()
)

alcohol_scotland_simple <- scotland_simple %>% 
  left_join(alcohol, by = c("HBCode" = "feature_code")) 

ggplot(alcohol_scotland_simple) +
  geom_sf(aes(group = hb_name, fill = value)) +
  theme_minimal() +
  theme(panel.grid.major = element_blank(), axis.text = element_blank()) +
  ggtitle("Alcohol Consumption in Scotland") + 
  scale_fill_gradient(low = "green", high = "red")

alcohol_scotland_simple %>% 
  leaflet() %>% 
  addTiles() %>%
  addMarkers(lng = ~longitude,
                   lat = ~latitude,
             popup = ~HBName)

obesity <- read_csv("../data_clean/obesity.csv") %>% 
  filter(sex == "All") %>% 
  filter(date_code == "2016-2019") %>% 
  filter(obesity == "Obese")

── Column specification ─────────────────────────────────────────────────────────────────────────────────────
cols(
  feature_code = col_character(),
  date_code = col_character(),
  measurement = col_character(),
  units = col_character(),
  value = col_double(),
  obesity = col_character(),
  sex = col_character(),
  hb_name = col_character(),
  longitude = col_double(),
  latitude = col_double()
)

scotland_simple %>% 
  left_join(obesity, by = c("HBCode" = "feature_code")) %>% 
ggplot() +
  geom_sf(aes(group = hb_name, fill = value)) +
  theme_minimal() +
  theme(panel.grid.major = element_blank(), axis.text = element_blank()) +
  ggtitle("Obesity in Scotland") + 
  scale_fill_gradient(low = "green", high = "red")

smoking_status <- read_csv("../data_clean/smoking_status.csv") %>% 
  filter(sex == "All") %>% 
  filter(date_code == "2016-2019") %>% 
  filter(smoking_status == "Current smoker")

── Column specification ─────────────────────────────────────────────────────────────────────────────────────
cols(
  feature_code = col_character(),
  date_code = col_character(),
  measurement = col_character(),
  units = col_character(),
  value = col_double(),
  smoking_status = col_character(),
  sex = col_character(),
  hb_name = col_character(),
  longitude = col_double(),
  latitude = col_double()
)

scotland_simple %>% 
  left_join(smoking_status, by = c("HBCode" = "feature_code")) %>% 
ggplot() +
  geom_sf(aes(group = hb_name, fill = value)) +
  theme_minimal() +
  theme(panel.grid.major = element_blank(), axis.text = element_blank()) +
  ggtitle("Smoking Status in Scotland") + 
  scale_fill_gradient(low = "green", high = "red")

e_cig <- read_csv("../data_clean/e_cig.csv") %>% 
  filter(sex == "All") %>% 
  filter(date_code == "2016-2019") %>% 
  filter(e_cigarette_use == "Currently using")

── Column specification ─────────────────────────────────────────────────────────────────────────────────────
cols(
  feature_code = col_character(),
  date_code = col_character(),
  measurement = col_character(),
  units = col_character(),
  value = col_double(),
  e_cigarette_use = col_character(),
  sex = col_character(),
  hb_name = col_character(),
  longitude = col_double(),
  latitude = col_double()
)

scotland_simple %>% 
  left_join(e_cig, by = c("HBCode" = "feature_code")) %>% 
ggplot() +
  geom_sf(aes(group = hb_name, fill = value)) +
  theme_minimal() +
  theme(panel.grid.major = element_blank(), axis.text = element_blank()) +
  ggtitle("E-cigarette use in Scotland") + 
  scale_fill_gradient(low = "green", high = "red")

alcohol %>% 
  filter(sex == "All") %>% 
  filter(hb_name == "Scotland") %>% 
    filter(date_code == "2016-2019") %>% 
  filter(alcohol_consumption == "Hazardous/Harmful drinker") 

alcohol_scotland

Error: object 'alcohol_scotland' not found